Pith Number

pith:4KALGOSH

pith:2026:4KALGOSHEMMPF2T62SH63XNVDN

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MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning

Chen Wang, Gi-joon Nam, Hua Xiang, Jinjun Xiong, Lei Zhuang, Ruiyang Qin, Yuting Hu

A neural model learns optimal photomasks by sequencing morphological operations on local layout features.

arxiv:2605.12528 v1 · 2026-04-13 · cs.CV · cs.AI · cs.AR

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Record completeness

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4 Citations open

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Claims

C1strongest claim

Experiments on edge-based OPC and ILT benchmarks across metal and via layers show that MorphOPC consistently outperforms state-of-the-art methods, achieving higher printing fidelity and lower manufacturing cost.

C2weakest assumption

That formulating mask generation as a sequence of morphological operations on local layout features allows the neural modules to accurately capture and learn the required geometric transformations from target layouts to optimal masks.

C3one line summary

MorphOPC proposes a multi-scale hierarchical neural model using morphological modules to generate optimized masks for optical proximity correction, outperforming prior generative methods on metal and via layer benchmarks.

References

35 extracted · 35 resolved · 0 Pith anchors

[1] Ahmed Awad, Atsushi Takahashi, Satoshi Tanaka, and Chikaaki Kodama. 2014. A fast process variation and pattern fidelity aware mask optimization algorithm. In2014 IEEE/ACM International Conference on C 2014

[2] Shayak Banerjee, Zhuo Li, and Sani R Nassif. 2013. ICCAD-2013 CAD contest in mask optimization and benchmark suite. In2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE, 271 2013

[3] Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. 2022. Swin-unet: Unet-like pure transformer for medical image segmentation. InEuropean conference on computer 2022

[4] Guojin Chen, Wanli Chen, Yuzhe Ma, Haoyu Yang, and Bei Yu. 2020. DAMO: Deep agile mask optimization for full chip scale. InProceedings of the 39th International Conference on Computer-Aided Design. 1– 2020

[5] Guojin Chen, Haoyu Yang, Haoxing Mark Ren, Bei Yu, and David Z Pan. 2024. Differentiable edge-based opc. InProceedings of the 43rd IEEE/ACM International Conference on Computer-Aided Design. 1–9 2024

Receipt and verification

First computed	2026-05-18T03:10:02.732044Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e280b33a472318f2ea7ed48fedddb51b6c4f59f9596e7a21c9fa154057e64bb8

Aliases

arxiv: 2605.12528 · arxiv_version: 2605.12528v1 · doi: 10.48550/arxiv.2605.12528 · pith_short_12: 4KALGOSHEMMP · pith_short_16: 4KALGOSHEMMPF2T6 · pith_short_8: 4KALGOSH

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4KALGOSHEMMPF2T62SH63XNVDN \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e280b33a472318f2ea7ed48fedddb51b6c4f59f9596e7a21c9fa154057e64bb8

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-13T14:40:34Z",
    "title_canon_sha256": "fda4d4fb3d15d77f5fed19713b8a88afa9ecc6eafa39276f26ab27de4220abd1"
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